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| Section | Weight | Objectives |
|---|---|---|
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe core machine learning concepts - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
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NEW QUESTION # 289
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore Azure Machine Learning", when you publish an inference pipeline (a deployed web service for real-time predictions) using Azure Machine Learning designer, you make the model accessible as a RESTful endpoint. Consumers-such as applications, scripts, or services-interact with this endpoint to submit data and receive predictions.
To securely access this deployed pipeline, two critical parameters are required:
* REST endpoint (Option D):The REST endpoint is a URL automatically generated when the inference pipeline is deployed. It defines the network location where clients send HTTP POST requests containing input data (usually in JSON format). The endpoint routes these requests to the deployed model, which processes the data and returns prediction results. The REST endpoint acts as the primary access point for consuming the model's inferencing capability programmatically.
* Authentication key (Option C):The authentication key (or API key) is a security token provided by Azure to ensure that only authorized users or systems can access the endpoint. When invoking the REST service, the key must be included in the request header (typically as the value of the Authorization header). This mechanism enforces secure, authenticated access to the deployed model.
The other options are incorrect:
* A. The model name is not required to consume the endpoint; it is used internally within the workspace.
* B. The training endpoint is used for training pipelines, not for inference.
Therefore, according to Microsoft's official AI-900 learning objectives and Azure Machine Learning documentation, when consuming a published inference pipeline, you must use both the REST endpoint (D) and the authentication key (C). These parameters ensure secure, controlled, and programmatic access to the deployed AI model for real-time predictions.
NEW QUESTION # 290
Match the Azure Al service to the appropriate generative Al capability.
To answer, drag the appropriate service from the column on the left to its capability on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
This question maps each Azure AI service to its correct capability based on the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn documentation on Azure Cognitive Services.
* Classify and label images # Azure AI VisionAzure AI Vision (formerly Computer Vision) provides capabilities to analyze visual content, detect objects, classify images, and extract information from pictures. It includes object detection, image classification, and tagging, which are core vision tasks.
This service enables businesses to build solutions that understand visual input, such as identifying products, reading signs, or detecting faces in images.
* Generate conversational responses # Azure OpenAI ServiceAzure OpenAI Service integrates powerful large language models such as GPT-3.5 and GPT-4, capable of generating human-like text responses, summarizations, translations, and dialogues. These models are designed for natural language generation (NLG) and conversational AI, making them ideal for chatbots, virtual agents, and intelligent assistants that produce dynamic, context-aware replies.
* Convert speech to text in real time # Azure AI SpeechAzure AI Speech provides speech-to-text capabilities (speech recognition) that convert spoken language into written text instantly. It is commonly used in transcription services, voice command systems, and live captioning applications.
Additionally, the Speech service supports text-to-speech (speech synthesis) and speech translation, making it versatile for voice-based AI applications.
By understanding each service's specialization-Vision for visual data, OpenAI for generative text, and Speech for audio processing-you can correctly match the capabilities.
NEW QUESTION # 291
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
For each of the following Statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 292
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
B: Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses the latest models and works with text on a variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two OCR APIs support extracting printed text in several languages.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
NEW QUESTION # 293
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 294
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